Autoscaling in an elastic cloud service

ABSTRACT

Techniques described herein can optimize usage of computing resources in a data system. Dynamic throttling can be performed locally on a computing resource in the foreground and autoscaling can be performed in a centralized fashion in the background. Dynamic throttling can lower the load without overshooting while minimizing oscillation and reducing the throttle quickly. Autoscaling may involve scaling in or out the number of computing resources in a cluster as well as scaling up or down the type of computing resources to handle different types of situations.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 63/202,769 filed Jun. 23, 2021, the contentsof which are incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to flexible computing, inparticular autoscaling and throttling in an elastic cloud service of adatabase system.

BACKGROUND

As the world becomes more data driven, database systems and other datasystems are storing more and more data. For a business to use this data,different operations or queries are typically run on this large amountof data. Some operations, for example those including large table scansor executing multiple queries, can take a substantial amount of time toexecute on a large amount of data. The time to execute such operationscan be proportional to the number of computing resources used forexecution, so time can be shortened using more computing resources.

To this end, some data systems can provide a pool of computingresources, and those resources can be assigned to execute differentoperations. However, in such systems, the assigned computing resourcestypically work in conjunction, for example in a cluster group. Hence,their assignments are fixed and static. That is, a computing resourcecan remain assigned to an operation, which no longer needs thatcomputing resource. The assignments of those computing resources cannotbe easily modified in response to demand changes. Hence, the computingresources are not utilized to their full capacity.

Some systems can impose fixed per-instance, per-account, and per-userlimits on the number of queries entering a single computing resource tolimit unexpected or periodic workloads and protect the cluster. However,these limits can be unreliable: some workloads that have a low querycardinality can fall below static limits but can still cause load issuesdepending on their compilation patterns.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which a clouddatabase system can implement streams on shared database objects,according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 shows an example of a data system configured for throttling andautoscaling, according to some example embodiments.

FIG. 5 illustrates a flow diagram for a method for throttling, accordingto some example embodiments.

FIG. 6 illustrates a flow diagram for a method for expansion, accordingto some example embodiments.

FIG. 7 illustrates synthetic workload running transaction processingcouncil-decision support (TPC-DS), according to some exampleembodiments.

FIG. 8 a flow diagram for a method for autoscaling, according to someexample embodiments.

FIG. 9 shows CPU load and cluster size monitoring in an internalanalysis cluster example, according to some example embodiments.

FIG. 10 shows successful queries and average concurrent requestmonitoring in an internal analysis cluster example, according to someexample embodiments.

FIG. 11 shows throttle coefficients monitoring in an internal analysiscluster example, according to some example embodiments.

FIG. 12 shows a cluster workload monitoring in a query concurrencyexample, according to some example embodiments.

FIG. 13 is an example of a cluster that has been scaled up dramaticallyto handle the increased workload, according to some example embodiments.

FIG. 14 shows CPU load monitoring in a deployment load example

FIG. 15 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Techniques described herein can optimize usage of computing resources ina data system. Dynamic throttling can be performed locally on acomputing resource in the foreground and autoscaling can be performed ina centralized fashion in the background. Dynamic throttling, asdescribed herein, can lower the load or usage without overshooting whileminimizing oscillation and reducing the throttle quickly. Autoscalingmay involve scaling in or out the number of computing resources in acluster as well as scaling up or down the type of computing resources tohandle different types of load situations.

FIG. 1 illustrates an example shared data processing platform 100. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted from thefigures. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the shareddata processing platform 100 to facilitate additional functionality thatis not specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based data warehouse system 102, a cloud computing storageplatform 104 (e.g., a storage platform, an AWS® service, MicrosoftAzure®, or Google Cloud Services®), and a remote computing device 106.The network-based data warehouse system 102 is a cloud database systemused for storing and accessing data (e.g., internally storing data,accessing external remotely located data) in an integrated manner, andreporting and analysis of the integrated data from the one or moredisparate sources (e.g., the cloud computing storage platform 104). Thecloud computing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based data warehouse system102. While in the embodiment illustrated in FIG. 1 , a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based data warehouse system 102.The remote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views, as discussed in further detail below.

The network-based data warehouse system 102 comprises an accessmanagement system 110, a compute service manager 112, an executionplatform 114, and a database 116. The access management system 110enables administrative users to manage access to resources and servicesprovided by the network-based data warehouse system 102. Administrativeusers can create and manage users, roles, and groups, and usepermissions to allow or deny access to resources and services. Theaccess management system 110 can store shared data that securely managesshared access to the storage resources of the cloud computing storageplatform 104 amongst different users of the network-based data warehousesystem 102, as discussed in further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager112 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based datawarehouse system 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-N that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-N may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (00M) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based data warehouse system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based data warehousesystem 102 and the cloud computing storage platform 104. The web proxy120 handles tasks involved in accepting and processing concurrent APIcalls, including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based data warehouse system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based data warehouse system 102 toscale quickly in response to changing demands on the systems andcomponents within network-based data warehouse system 102. Thedecoupling of the computing resources from the data storage devices124-1 to 124-N supports the storage of large amounts of data withoutrequiring a corresponding large amount of computing resources.Similarly, this decoupling of resources supports a significant increasein the computing resources utilized at a particular time withoutrequiring a corresponding increase in the available data storageresources. Additionally, the decoupling of resources enables differentaccounts to handle creating additional compute resources to process datashared by other users without affecting the other users' systems. Forinstance, a data provider may have three compute resources and sharedata with a data consumer, and the data consumer may generate newcompute resources to execute queries against the shared data, where thenew compute resources are managed by the data consumer and do not affector interact with the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based data warehousesystem 102 is dynamic and supports regular changes to meet the currentdata processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1 , the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-N in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , a request processing service 202manages received data storage requests and data retrieval requests(e.g., jobs to be performed on database data). For example, the requestprocessing service 202 may determine the data necessary to process areceived query (e.g., a data storage request or data retrieval request).The data may be stored in a cache within the execution platform 114 orin a data storage device in cloud computing storage platform 104. Amanagement console service 204 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 204 may receive a request to execute a joband monitor the workload on the system. The stream share engine 225manages change tracking on database objects, such as a data share (e.g.,shared table) or shared view, according to some example embodiments, andas discussed in further detail below.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based data warehouse system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based data warehouse system 102. For example, data storagedevice 220 may represent caches in execution platform 114, storagedevices in cloud computing storage platform 104, or any other storagedevice.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , execution platform 114 includesmultiple virtual warehouses, which are elastic clusters of computeinstances, such as virtual machines. In the example illustrated, thevirtual warehouses include virtual warehouse 1, virtual warehouse 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 124-1 to124-N and, instead, can access data from any of the data storage devices124-1 to 124-N within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-N. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

Next, dynamic throttling and autoscaling techniques will be described.FIG. 4 shows an example of a data system 400 configured for throttlingand autoscaling, according to some example embodiments. The data system400 may include a plurality of foreground clusters 410, 420, 430. Eachforeground cluster may include a plurality of computing resources (alsoreferred to as nodes, instances, servers). In this example, foregroundcluster 410 may include computing resources 412.1-412.3; foregroundservice 420 may include computing resources 422.1-422.5; and foregroundservice 430 may include computing resources 432.1-432.4. The foregroundclusters may have different number of computing resources, and thenumber of computing resources assigned to each cluster may change basedon the autoscaling techniques, described in further detail below.

A foreground cluster may be assigned to a group of accounts in amulti-tenancy embodiment. A foreground cluster may be assigned to asingle account in a dedicated-cluster embodiment. The foregroundclusters may receive requests or queries and develop query plans toexecute the queries. The foreground clusters may broker requests to itscomputing resources that execute a query plan. The foreground clustersmay receive query requests from different sources, which may havedifferent account IDs. For certain operations, such as those involvingmultiple computing resources working together to execute differentportions of an operation (e.g., large table scans), the source may bedefined at a data warehouse level granularity.

The computing resources may be computing nodes allocated to theforeground GS 400 from a pool of computing nodes. In an embodiment, thecomputing resources may be machines, servers, CPUs, and/or processors.

The clusters 410, 420, 430 may communicate with a centralized autoscaler450 over a network. In an embodiment, communications between theclusters 410, 420, 430 and autoscaler 450 may be performed via ametadata database 440. That is, the components in the clusters 410, 420,430 may transmit messages, for example relating to their currentworkloads, to the metadata database 440, where the information fromthose messages may be stored. The reverse proxy server can routerequests across the components in the clusters 410, 420, 430 and theautoscaler 450 may read the information sent by the clusters 410, 420,430 from the metadata database 440.

In another embodiment, communications between the clusters 410, 420, 430and the autoscaler 450 may be performed directly via, for example,remote procedure calls such as gRPCs. Moreover, communications betweenthe clusters 410, 420, 430 and the autoscaler 450 may be performed usinga combination of direct communication (e.g., remote procedure calls) andindirect communications (e.g., via metadata database).

The autoscaler 450 may be coupled to a cloud resource provider. Thecloud resource provider may maintain a pool of computing resources. Thecomputing resources may be of different types and have differentspecifications, as described in further detail below. In an embodiment,the autoscaler 450 may communicate with a communication layer over thecloud resource provider.

Dynamic throttling in foreground instances may be coupled withcentralized autoscaling, which runs in a background instance. Dynamicthrottling may be implemented by each computing resource of a datasystem (e.g., computing resources 412.1-412.3, 422.1-422.5, 432.1-432.4of data system 400). Throttling may be localized at each node and maynot need communications with other nodes to be implemented.

FIG. 5 illustrates a flow diagram for a method 500 for throttling,according to some example embodiments. Method 500 may be executed by acomputing resource, as described herein.

At operation 502, a computing resource may monitor one or more resourceusage. The resource usage(s) may include load usage, memory usage,saturation level, and other computing-type resources or a combinationthereof. Load usage (also called CPU usage) may correspond to a numberof runnable tasks divided by existing cores/processors. In other words,it is the number of tasks to be executed divided by the number of thingsthat can execute those tasks. Memory may correspond to local memory atthe computing resource.

At operation 504, the computing resource may detect that the one or moremonitored resource usage has exceeded a maximum threshold. The maximumthreshold may be predefined by a network administrator and may be basedon the specification of the computing resource.

At operation 506, the computing resource may generate an estimate of newinbound requests (e.g., queries) that should be allowed to be processedby the computing resource to lower the resource usage to below themaximum threshold (also referred to as allotment of new inboundrequests). The computing resource may execute and not disturb pendingrequests so that the throttling only impacts inbound requests. Theestimate may take into consideration the current amount of requestspending. For example, consider a scenario where the maximum threshold ofload usage is set to 1; the current load usage is 2.0; and there are 10current jobs/requests pending. In this example scenario, the estimatemay be reducing the new inbound requests to 5 jobs/requests may reducethe load to below the maximum threshold.

At operation 508, a gateway limit of new inbound requests may be set forthe computing resource. The gateway limit may be based on the estimateto lower the resource usage to below the maximum threshold. Inboundrequests over the gateway limit may be rejected by the computingresource. The rejection may include an instruction to the client toretry the request that was rejected after some time. Thus, the requestwould be resent by the client after some time and may then be processed.For example, the request when it is resent may be assigned to adifferent computing resource. In some embodiments, the computingresources may be assigned new requests in a round robin fashion (or viause of another scheduling algorithm) by a reverse proxy server. Inanother example, that request may be sent to the same computingresource, which may now have the bandwidth to handle the request.

At operation 510, for a defined time interval (e.g., three minutes), thecomputing resource may periodically (e.g., every thirty seconds) adjustthe gateway limit based on current resource usage. That is, thecomputing resource may monitor the current resource usage and may adjustthe gateway limit up or down accordingly. The adjustment may beincremental in that the adjustment may be a set amount for eachperiodical adjustment (e.g., 10%). This may reduce oscillation.

At operation 512, at the end of the defined time interval, the computingresource may determine whether to continue throttling or not. Thisdetermination may be based on current resource usage as monitored by thecomputing resource and the current rejection rate of new inboundrequests.

If it is determined that the throttling should continue, the method mayjump to operation 506 and a new estimate of new inbound requests (e.g.,queries) that should be allowed to be processed by the computingresource to lower the resource usage. Hence, the method may then performincremental adjustments (operations 508-510) for another defined timeinterval (e.g., 3 minutes).

If it is determined that throttling is no longer needed, a full recoverymay be implemented at operation 514. Full recovery may includeeliminating the gateway limits on new inbound requests and setting thelimit to its default value. Moreover, the computing resource may thenrevert to operation 502 and monitor the one or more resource usage.

The gateway limits set in throttling may be customized or scaled on anaccount and/or user level. In a multi-tenancy embodiment, the computingresource may implement the gateway limit using a fairness algorithm onan account basis. For example, the gateway limit may include a provisionthat no account can use more than a set percentage (e.g., 50%) of theallotment of new inbound requests. The gateway limit may be appliedevenly causing all accounts to reduce the new requests evenly.Therefore, the account with the highest job account may be the first tobe limited.

In an example, two types of throttling coefficients may be used. Thefirst is the instance coefficient that adjusts the amount of requeststhat each individual instance can receive. The second coefficient is anaccount level coefficient that adjusts the total number of concurrentrequests an account can run in its cluster. These coefficients may bedynamically updated at routine intervals (e.g., every 30 seconds).

Moreover, the gateway limit may also be set on a user level in a singletenant environment and a multitenant environment. For example, thegateway limit may include a provision that no user of an account can usemore than a set percentage (e.g., 25%) of that account's allotment ofnew inbound requests.

Moreover, computing resources may be configured to perform expansioninstead of throttling based on monitored conditions. FIG. 6 illustratesa flow diagram for a method 600 for expansion, according to some exampleembodiments. Method 600 may be executed by a computing resource, asdescribed herein.

At operation 602, a computing resource may monitor one or more resourceusage(s). The resource usage(s) may include load usage, memory usage,saturation level, and other computing-type resources.

At operation 604, the computing resource may detect that the one or moremonitored resource usage is below a minimum threshold. The minimumthreshold may be predefined by a network administrator and may be basedon the specification of the computing resource.

At operation 606, the computing resource may monitor a rejection rate ofnew inbound requests and may compare the rejection rate to a set rate.

At 608, if the rejection rate is higher than the set rate, the computingresource may increase the number of inbound requests it will accept.Hence, if the resource usage is below a minimum threshold and therejection rate of inbound requests is still above a set rate, thecomputing resource may increase the amount of acceptable inboundrequests to further optimize its performance and usage.

FIG. 7 illustrates a synthetic workload running transaction processingcouncil-decision support (TPC-DS) which analyzes the performance ofonline analytical processing (OLAP) databases. In this test environment,dynamic throttling is active with a cluster configured to have twocompute service instances. The generated workload exceeded the CPUcapacity of two compute service instances. Dynamic throttling reacted tothe excessive load and reduced the gateway limits to maintain thehealthy state of the available instances. In the top left (702), the CPUload of the two instances is reported. It is observed that the loadexceeded the available CPU capacity. This is highly correlated with thequery count in the bottom left graph (704). The top right graph (706)shows the throttle coefficient of the throttled being lowered to enforcea new gateway size, effectively applying a small multiplier to ourgateways. Finally, after the throttler determines the new gateway limit,fewer queries are let through. In the bottom left graph (708), the querythroughput drops to a sustainable amount, which also causes a drop inthe associated load the instances face.

Furthermore, after the initial estimation the coefficient in the topright graph (706) can be seen to adjust incrementally. It initiallydrops lower to temper the workload further, and maintains a balancekeeping the CPU load at approximately 1.0, which is the target.

The bottom right graph (708) displays the coefficient used for account &user specific limits. To maintain fairness, the limit is applied evenly,causing all accounts to reduce evenly. Therefore, the account with thehighest job count will be the first to be limited. It should be notedthe limitation of dynamic throttling is transient and upon rejectingthese queries a signal is provided to the autoscaling framework to addinstances to overloaded clusters, as described in further detail below.This can temper transient load spikes and prevent them from causingdownstream issues in the database system until their workload can beaccommodated.

Autoscaling may be performed in a centralized fashion. Autoscaling mayinclude scaling in and out computing resources. Scaling in refers toremoving computing resources from a cluster, and scaling out refers toadding computing resources to a cluster. Autoscaling may also includescaling up and down computing resources. Scaling up refers to changingthe computing resources of a cluster with computing resources with ahigher level, and scaling down refers to changing the computingresources of a cluster with computing resources with a lower level.These higher or lower levels may refer to specification aspects of thecomputing resources such as memory capacity, for example.

FIG. 8 illustrates a flow diagram for a method 800 for autoscaling,according to some example embodiments. Method 800 may be executed by anautoscaler, as described herein.

At operation 802, an autoscaler may receive information and statisticsrelated to usage levels of the computing resources in differentclusters. This information may include usage level on a per-node basisand may include results of dynamic throttling being performed by eachnode. This information received may include load average, rate ofrejections (e.g., total number of rejections/number of requests), etc.

This information may also include notification of garbage collection(GC) moments, such as a full GC moment, and out-of-memory errors. Theinformation may also include an indication of any requests that may havebeen terminated. For example, if a query is received and the parse treefor that query is relatively large and is memory intensive, that querymay be terminated before execution to prevent other errors such as anout-of-memory error.

At operation 804, based on the information received, a plurality ofdifferent moving average windows may be generated. In one embodiment,three moving average windows may be generated. A short window (e.g., 1minute) may be based on information relating to the system and nodeconditions for the last minute. A medium window (e.g., 5 minutes) may bebased on information relating to the system and node conditions for thelast five minutes. A long window (e.g., 15 minutes) may be based oninformation relating to the system and node conditions for the last 15minutes.

At operation 806, based on the information received and the differentmoving average windows, the autoscaler may adjust the number ofcomputing resources assigned to each cluster. The autoscaler may scalein or out (or keep the same) the number of computing resources for thecluster. Scaling in refers to adding computing resources to a cluster,and scaling out refers to removing computing resources from a cluster.

Scaling in or out may be performed according to the following formula:

${desiredNodes} = \left\lceil {{currentNodes} \star \frac{currentMetricValue}{desiredMetricValue}} \right.$

desiredNodes refers to the optimized number of nodes for the cluster.currentNodes refers to the current number of nodes assigned to thatcluster. currentMetricValue refers to the current monitored resourceusage value (e.g., load value, memory usage), and desiredMetricValuerefers to the threshold usage value. For example: if currentNodes=30 anddesiredMetricValue for load is 1.0. If currentMetricValue drops to 0.98,then the system may scale in by 1 instance.

Scaling in or out may be done incrementally. For example, the autoscalermay scale out by a maximum of two nodes in a given iteration. Theautoscaler may scale in by a maximum of one node in a given iteration.These limits may help avoid oscillation.

Different moving average windows may be considered for scaling in andout. For example, to scale out, only the short window (e.g., 1 minute)may be analyzed to determine whether it is high. To scale in, aplurality of windows, such as all windows (e.g., 1, 5, and 15 minutes),may be analyzed to determine whether all three windows are low.

At operation 808, based on the information received and the differentmoving average windows, the autoscaler may adjust the type of computingresources assigned to each cluster. One node type may be assigned percluster. That is, all computing resources of a cluster may be the sametype. The autoscaler may scale up or down (or keep the same) the type ofcomputing resources for the cluster. Scaling up refers to changing thecomputing resources of a cluster with computing resources with a higherlevel and scaling down refers to changing the computing resources of acluster with computing resolves with a lower level. These higher orlower levels may refer to specification aspects of the computingresources such as memory capacity, for example.

Scaling up or down may address situations where adding or removingcomputing resources may not adequately address. These situations mayinclude memory issues. Simply adding or removing computing resources maynot adequately address memory issues facing those computing resources.For example, if a computing resource is determined to have not enoughmemory to perform certain tasks, the autoscaler may scale up thecomputing resources for that cluster to computing resources with largermemories. In another example, the autoscaler may scale down thecomputing resources of a cluster if it is determined that the tasksassigned to that cluster can be performed with computing resources withsmaller memories or other specification aspects. Scaling down may reducecosts.

High and low thresholds may be set to determine the best suitedcomputing resource for each cluster. This may be done predictively basedon the high and low thresholds so a critical failure is not reached.Moreover, historical trends, such as recent history, may be taken intoconsideration in the selection.

The autoscaling techniques described herein can be effective inoptimizing usage of computing resources in a data system. They can beeffective against recurring bursty workloads followed by long periods ofidleness or random bursts (e.g., bursts are distributedrandomly/non-uniformly over a time window). They can efficiently handledifferent types of queries. For example, some queries can beshort-lived, some are long-running and these ones can occupy a computeservice node for hours. The autoscaling techniques described herein canaccount for these different types of queries.

Example 1—An Internal Analysis Cluster

Autoscaling and dynamic throttling, as described herein, are synergisticand therefore can be combined. When enabling the features on an internaldata analysis account scaling was performed in the cluster. FIG. 9 showsCPU load and cluster size monitoring in an internal analysis clusterexample. FIG. 9 shows the cluster instance count in the bottom chart.The cluster was reduced to two instances as the scaling frameworkrecognizes the compute resources of all four instances were notrequired.

FIG. 10 shows successful queries and average concurrent requestmonitoring in an internal analysis cluster example. In FIG. 10 , it canbe seen that two of the lines, each representing an instance, drop toserving 0 queries per second as they are removed from topology. Thequeries per second and concurrent requests of the remaining twoinstances increases as the same overall cluster throughput is maintainedwith two instances.

FIG. 11 shows throttle coefficients monitoring in an internal analysiscluster example. With dynamic throttling, the throttling coefficientswere expanded automatically, providing additional gateway space forqueries that could be handled safely. This removed any manual work toscale instances or gateway limits making the platform more resilient.Both the instance and account gateways were expanded which allowed thesystem to facilitate a higher throughput on each individual instance.Now the system, in this example, recognizes when clusters areoverprovisioned and can reduce it to optimize resource usage.

Example 2—Query Concurrency Increased

Many clusters can run into previously static gateway limits.

However, using the techniques described herein, these clusters canincrease their total concurrency and their rejection rate was reduced.

FIG. 12 shows a cluster workload monitoring in a query concurrencyexample. The top chart shows the account coefficient, which in this casewill be greater than 1 when the respective gateways are expanded. Byusing the techniques described herein, around 3 pm, the rejection rate,in the bottom chart, was reduced and concurrent requests increased whichcan be seen in the center chart. It shall be appreciated that theincrease to the coefficient was not high, which may have eliminated therejections entirely.

Example 3—Noisy Neighbor

Oftentimes “noisy neighbor” problems are encountered. A “noisy neighbor”occurs when a customer in a multi-tenant cluster begins a massiveworkload that starves other accounts in the cluster of resources. Byusing the autoscaling and dynamic throttling the database system is ableto scale the cluster very quickly. FIG. 13 is an example of a clusterthat has been scaled up dramatically to handle the increased workload.The cluster size chart represents the time the decision was made, notthe actual virtual machine (VM) being moved into the cluster after thecache warmed and moved into topology.

In FIG. 13 , it can be seen in the cluster size chart that previous tothe noisy neighbor the traffic was low enough for the autoscaling systemto decide to reduce the instance count to two. The throughput of thecluster was quadrupled from instances on hand in the free pool.

Example 4—Deployment Load

An objective of the dynamic throttling is to reduce cases of high CPUload that instances encounter. FIG. 14 shows CPU load monitoring in adeployment load example. By using dynamic throttling, the 5-minute CPUload average charts lowered noticeably, in particular almost no nodeshad a 5 minute CPU load higher than the target threshold of 1. In FIG.14 , the rollout occurs as the coefficient spreads, around 6:30 pm. The5-minute CPU load average chart, showing the load average for everyindividual load in the deployment, no longer has cases where the loadexceeds our target threshold. This effect was noticed across all of alldeployments, with a few exceptions of particular instances managing toexceed this target. The end result of this has been a decrease inisolations based on high load average.

FIG. 15 illustrates a diagrammatic representation of a machine 1500 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1500 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 15 shows a diagrammatic representation of the machine1500 in the example form of a computer system, within which instructions1516 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1500 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1516 may cause the machine 1500 to execute anyone or more operations of any one or more of the methods describedherein. As another example, the instructions 1516 may cause the machine1500 to implement portions of the data flows described herein. In thisway, the instructions 1516 transform a general, non-programmed machineinto a particular machine 1500 (e.g., the remote computing device 106,the access management system 158, the compute service manager 152, theexecution platform 154, the access management system 158, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 1500 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1500 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1500 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1516, sequentially orotherwise, that specify actions to be taken by the machine 1500.Further, while only a single machine 1500 is illustrated, the term“machine” shall also be taken to include a collection of machines 1500that individually or jointly execute the instructions 1516 to performany one or more of the methodologies discussed herein.

The machine 1500 includes processors 1510, memory 1530, and input/output(I/O) components 1550 configured to communicate with each other such asvia a bus 1502. In an example embodiment, the processors 1510 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 1512 and aprocessor 1514 that may execute the instructions 1516. The term“processor” is intended to include multi-core processors 1510 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 1516 contemporaneously. AlthoughFIG. 15 shows multiple processors 1510, the machine 1500 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 1530 may include a main memory 1532, a static memory 1534,and a storage unit 1536, all accessible to the processors 1510 such asvia the bus 1502. The main memory 1532, the static memory 1534, and thestorage unit 1536 store the instructions 1516 embodying any one or moreof the methodologies or functions described herein. The instructions1516 may also reside, completely or partially, within the main memory1532, within the static memory 1534, within the storage unit 1536,within at least one of the processors 1510 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1500.

The I/O components 1550 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 1550 thatare included in a particular machine 1500 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 1550 mayinclude many other components that are not shown in FIG. 15 . The I/Ocomponents 1550 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1550 mayinclude output components 1552 and input components 1554. The outputcomponents 1552 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 1554 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1550 may include communication components 1564operable to couple the machine 1500 to a network 1580 or devices 1570via a coupling 1582 and a coupling 1572, respectively. For example, thecommunication components 1564 may include a network interface componentor another suitable device to interface with the network 1580. Infurther examples, the communication components 1564 may include wiredcommunication components, wireless communication components, cellularcommunication components, and other communication components to providecommunication via other modalities. The devices 1570 may be anothermachine or any of a wide variety of peripheral devices (e.g., aperipheral device coupled via a universal serial bus (USB)). Forexample, as noted above, the machine 1500 may correspond to any one ofthe remote computing device 106, the access management system 150, thecompute service manager 152, the execution platform 154, the accessmanagement system 158, the Web proxy 120, and the devices 1570 mayinclude any other of these systems and devices.

The various memories (e.g., 1530, 1532, 1534, and/or memory of theprocessor(s) 1510 and/or the storage unit 1536) may store one or moresets of instructions 1516 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions 1516, when executed by theprocessor(s) 1510, cause various operations to implement the disclosedembodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 1580may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 1580 or a portion of the network1580 may include a wireless or cellular network, and the coupling 1582may be a Code Division Multiple Access (CDMA) connection, a GlobalSystem for Mobile communications (GSM) connection, or another type ofcellular or wireless coupling. In this example, the coupling 1582 mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High-Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard-setting organizations,other long-range protocols, or other data transfer technology.

The instructions 1516 may be transmitted or received over the network1580 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1564) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1516 may be transmitted or received using a transmission medium via thecoupling 1572 (e.g., a peer-to-peer coupling) to the devices 1570. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1516 for execution by the machine 1500, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

The following numbered examples are embodiments:

Example 1. A method comprising: monitoring resource usage at a computingresource; determining that the resource usage has exceeded a maximumthreshold; generating an estimate of an allotment of new inboundrequests to be received by the computing resource to lower the resourceusage below the maximum threshold; setting a gateway limit of newinbound requests based on the estimate to throttle new inbound requestsreceived by the computing resource; for a defined time interval,periodically adjusting the gateway limit based on a current resourceusage; and after the defined time interval, implementing a full recoveryof the gateway limit.

Example 2. The method of example 2 wherein the estimate of the allotmentis a first estimate and wherein the defined time interval is a firstdefined time interval, further comprising: after the defined timeinterval, generating a second estimate of the allotment of new inboundrequests to be received by the computing resource to lower the resourceusage below the maximum threshold; setting the gateway limit based onthe second estimate to throttle new inbound requests received by thecomputing resource; for a second defined time interval, periodicallyadjusting the gateway limit based on the current resource usage; andafter the second defined time interval, implementing the full recoveryof the gateway limit.

Example 3. The method of any of examples 1-2, further comprising:rejecting new inbound requests over the gateway limit.

Example 4. The method of any of examples 1-3, further comprising:transmitting an instruction to a sender of a rejected inbound request tore-send the request after a set time.

Example 5. The method of any of examples 1-4, wherein the gateway limitis defined on a per-account basis.

Example 6. The method of any of examples 1-5, wherein the resource usageincludes central processing unit load.

Example 7. The method of any of examples 1-6, wherein the resource usageincludes memory capacity of the computing resource.

Example 8. The method of any of examples 1-7, determining that theresource usage is below a minimum threshold; determining that arejection rate of new inbound requests is above rejection threshold; andexpanding the allotment of new inbound requests to be received by thecomputing resource.

Example 9. A system comprising: one or more processors of a machine; anda memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 8.

Example 10. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 8.

1. A method comprising: receiving information and statistics of usagelevels of computing resources in a plurality of clusters, theinformation including notifications of memory error events; generating aplurality of moving average windows of usage levels of the computingresources in each of the plurality of clusters, each of the plurality ofmoving average windows being for different, overlapping time durations;based on the plurality of moving average windows, adjusting a number ofcomputing resources assigned to the plurality of clusters includingdecreasing the number of computing resources to a first of the pluralityof clusters based on all of the plurality of moving average windows andincreasing the number of computing resources assigned to a second of theplurality of clusters based on a single window of the plurality ofmoving average windows; and adjusting a type of computing resourcesassigned to at least one of the plurality of clusters based on thenotifications of memory error events.
 2. The method of claim 1, whereinthe information includes load average of each computing resource.
 3. Themethod of claim 1, wherein the information includes a rate of rejectionsby each computing resource.
 4. The method of claim 1, wherein theplurality of moving average windows includes three moving averagewindows of different durations. 5.-6. (canceled)
 7. The method of claim1, wherein the type of computing resources is associated with memorycapacity.
 8. The method of claim 1, wherein adjusting the type ofcomputing resources assigned to a respective cluster is based on a typeof tasks assigned to the respective cluster.
 9. A machine-storage mediumembodying instructions that, when executed by a machine, cause themachine to perform operations comprising: receiving information andstatistics of usage levels of computing resources in a plurality ofclusters, the information including notifications of memory errorevents; generating a plurality of moving average windows of usage levelsof the computing resources in each of the plurality of clusters, each ofthe plurality of moving average windows being for different, overlappingtime durations; based on the plurality of moving average windows,adjusting a number of computing resources assigned to the plurality ofclusters including decreasing the number of computing resources to afirst of the plurality of clusters based on all of the plurality ofmoving average windows and increasing the number of computing resourcesassigned to a second of the plurality of clusters based on a singlewindow of the plurality of moving average windows; and adjusting a typeof computing resources assigned to at least one of the plurality ofclusters based on the notifications of memory error events.
 10. Themachine-storage medium of claim 9, wherein the information includes loadaverage of each computing resource.
 11. The machine-storage medium ofclaim 9, wherein the information includes a rate of rejections by eachcomputing resource.
 12. The machine-storage medium of claim 9, whereinthe plurality of moving average windows includes three moving averagewindows of different durations. 13.-14. (canceled)
 15. Themachine-storage medium of claim 9, wherein the type of computingresources is associated with memory capacity.
 16. The machine-storagemedium of claim 9, wherein adjusting the type of computing resourcesassigned to a respective cluster is based on a type of tasks assigned tothe respective cluster.
 17. A system comprising: at least one hardwareprocessor; and at least one memory storing instructions that, whenexecuted by the at least one hardware processor, cause the at least onehardware processor to perform operations comprising: receivinginformation and statistics of usage levels of computing resources in aplurality of clusters, the information including notifications of memoryerror events; generating a plurality of moving average windows of usagelevels of the computing resources in each of the plurality of clusters,each of the plurality of moving average windows being for different,overlapping time durations; based on the plurality of moving averagewindows, adjusting a number of computing resources assigned to theplurality of clusters including decreasing the number of computingresources to a first of the plurality of clusters based on all of theplurality of moving average windows and increasing the number ofcomputing resources assigned to a second of the plurality of clustersbased on a single window of the plurality of moving average windows; andadjusting a type of computing resources assigned to at least one of theplurality of clusters based on the notifications of memory error events.18. The system of claim 17, wherein the information includes loadaverage of each computing resource.
 19. The system of claim 17, whereinthe information includes a rate of rejections by each computingresource.
 20. The system of claim 17, wherein the plurality of movingaverage windows includes three moving average windows of differentdurations. 21.-22. (canceled)
 23. The system of claim 17, wherein thetype of computing resources is associated with memory capacity.
 24. Thesystem of claim 17, wherein adjusting the type of computing resourcesassigned to a respective cluster is based on a type of tasks assigned tothe respective cluster.